Determination of a type of destage to perform based on preference between performance of operations and preservation of drive life using a machine learning module

ABSTRACT

A storage controller is configured to perform a full stride destage, a strip destage, and an individual track destage. A machine learning module receives a plurality of inputs corresponding to a plurality of factors that affect performance of data transfer operations and preservation of drive life in the storage controller. In response to receiving the inputs, the machine learning module generates a first output, a second output, and a third output that indicate a preference measure for the full stride destage, the strip destage, and the individual track destage respectively.

BACKGROUND 1. Field

Embodiments relate to the determination of a type of destage to performbased on preference between performance of operations and preservationof drive life using a machine learning module.

2. Background

In certain storage system environments, a storage controller (or astorage controller complex) may comprise a plurality of storage serversthat are coupled to each other. The storage controller allows hostcomputing systems to perform input/output (I/O) operations with storagedevices controlled by the storage controller, where the host computingsystems may be referred to as hosts.

The storage controller may include two or more servers, where eachserver may be referred to as a node, a storage server, a processorcomplex, a Central Processor Complex (CPC), or a Central ElectronicsComplex (CEC). Each server may have a plurality of processor cores andthe servers may share the workload of the storage controller. In a twoserver configuration of the storage controller that is also referred toas a dual-server based storage controller, in the event of a failure ofone of the two servers, the other server that has not failed may takeover the operations performed by the failed server.

Data written from a host may be stored in the cache of the storagecontroller, and at an opportune time the data stored in the cache may bedestaged (i.e., moved or copied) to a storage device. Data may also bestaged (i.e., moved or copied) from a storage device to the cache of thestorage controller. The storage controller may respond to a read I/Orequest from the host from the cache, if the data for the read I/Orequest is available in the cache, otherwise the data may be staged froma storage device to the cache for responding to the read I/O request. Awrite I/O request from the host causes the data corresponding to thewrite to be written to the cache, and then at an opportune time thewritten data may be destaged from the cache to a storage device. Sincethe storage capacity of the cache is relatively small in comparison tothe storage capacity of the storage devices, data may be periodicallydestaged from the cache to create empty storage space in the cache. Datamay be written and read from the cache much faster in comparison toreading and writing data from a storage device.

In computer data storage, data striping is the technique of segmentinglogically sequential data, such as a file, so that consecutive segmentsare stored on different physical storage devices such as disks. Thesegments of sequential data written to or read from a disk before theoperation continues on the next disk are usually called chunks, stridesor stripe units, while their logical groups forming single stripedoperations are called strips or stripes.

Striping is used across disk drives in Redundant array of Independentdisks (RAID) storage. RAID is a data storage virtualization technologythat combines multiple physical disk drive components into one or morelogical units for the purposes of data redundancy and performanceimprovement. Data is distributed across the drives in one of severalways, referred to as RAID levels, depending on the required level ofredundancy and performance. The different schemes, or data distributionlayouts, are named by the word “RAID” followed by a number, for exampleRAID 0 or RAID 1. Each scheme, or RAID level, provides a differentbalance among the key goals of reliability, availability, performance,and capacity. RAID levels greater than RAID 0 provide protection againstunrecoverable sector read errors, as well as against failures of wholephysical drives via parity information that is maintained for storeddata. A strip is a term that is related to a single disk, and is apredefined number of contiguous addressable blocks in that disk. Astripe comes in action in case of a RAID set, and it is the set ofstrips spanning across all the drives in that RAID set.

Artificial neural networks (also referred to as neural networks) arecomputing systems that may have been inspired by the biological neuralnetworks that constitute animal brains. Neural networks may beconfigured to use a feedback mechanism to learn to perform certaincomputational tasks. Neural networks are a type of machine learningmechanism.

SUMMARY OF THE PREFERRED EMBODIMENTS

Provided are a method, system, and computer program product in which astorage controller is configured to perform a full stride destage, astrip destage, and an individual track destage. A machine learningmodule receives a plurality of inputs corresponding to a plurality offactors that affect performance of data transfer operations andpreservation of drive life in the storage controller. In response toreceiving the inputs, the machine learning module generates a firstoutput, a second output, and a third output that indicate a preferencemeasure for the full stride destage, the strip destage, and theindividual track destage respectively.

In further embodiments a determination of which type of destage toperform based on the first output, the second output, and the thirdoutput.

In additional embodiments, the plurality of factors includesinput/output (I/O) operations and bandwidth on a rank, non-volatilestorage (NVS) capacity, parity lock contention, holes, and unmodifieddata in a stride.

In yet additional embodiments, the plurality of factors also includes awear level of a drive, writes per day classification of the drive, andan amount of modified data in the stride.

In further embodiments, computing of a margin of error to adjust weightsin the machine learning module includes comparing a maximum bandwidth toa current bandwidth, comparing a maximum I/O operations per second(IOPS) to a current IOPS, comparing an optimal drive write measure to ameasure of actual writes, and comparing a measure of optimal parity lockcontention to a parity lock contention.

In additional embodiments, computing the margin of error to adjust theweights in the machine learning module further includes weightingadjustments to train the machine learning module based on a firstweightage provided for the performance of data transfer operations and asecond weightage provided for the preservation of drive life.

In further embodiments, the machine learning module is a neural networkwith one or more hidden layers, and wherein forward and backwardpropagation mechanisms are utilized to adjust weights in the neuralnetwork.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 illustrates a block diagram of a computing environment comprisinga storage controller coupled to one or more hosts and one or morestorage devices, in accordance with certain embodiments;

FIG. 2 illustrates a block diagram that shows the types of destages thatmay be performed by the storage controller, in accordance with certainembodiments;

FIG. 3 illustrates a block diagram that shows a multi-output machinelearning module for balancing performance with preservation of drivelife, in accordance with certain embodiments;

FIG. 4 illustrates a block diagram that shows inputs for the machinelearning module, in accordance with certain embodiments;

FIG. 5 illustrates a block diagram that shows outputs for the machinelearning module;

FIG. 6 illustrates a block diagram that shows training of the machinelearning module;

FIG. 7 illustrates a block diagram that shows factors for calculatingmargin of error in the machine learning module, in accordance withcertain embodiments;

FIG. 8 illustrates a block diagram that adjustments made in machinelearning module based on bandwidth, in accordance with certainembodiments;

FIG. 9 illustrates a block diagram that shows adjustments made in amachine learning module based on I/O operations per second (IOPS), inaccordance with certain embodiments;

FIG. 10 illustrates a block diagram that shows adjustments made in amachine learning module based on drive writes, in accordance withcertain embodiments;

FIG. 11 illustrates a block diagram that shows adjustments made in amachine learning module based on parity lock contention, in accordancewith certain embodiments;

FIG. 12 illustrates a block diagram that shows adjusting of weights fortraining the machine learning module based on weights provided by a userfor performance and drive life, in accordance with certain embodiments;

FIG. 13 illustrates a flowchart for selecting a type of destage toperform by the machine learning module, in accordance with certainembodiments;

FIG. 14 illustrates a block diagram of a cloud computing environment, inaccordance with certain embodiments;

FIG. 15 illustrates a block diagram of further details of the cloudcomputing environment of FIG. 14, in accordance with certainembodiments; and

FIG. 16 illustrates a block diagram of a computational system that showscertain elements that may be included in the storage controller or thehost, as described in FIGS. 1-15, in accordance with certainembodiments.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingdrawings which form a part hereof and which illustrate severalembodiments. It is understood that other embodiments may be utilized andstructural and operational changes may be made.

In certain embodiments, a storage controller may perform full stridedestages or destages that are not full stride destages (e.g., stripdestage or individual track destage) based on various factors thataffect performance of destages and drive life. In a full stride destageall of the data and the parity in all of the strides (or stripes) arewritten during destage. In a strip destage, one or more strips arewritten during destage. In a track destage, one or more tracks arewritten during destage.

While performing destages, a number of factors may affect theperformance of destages and the drive life, where the performance refersto how fast destages are completed, and the drive life refers to thelife expectancy or endurance of the drives. Such factors that affectperformance of the destages and the drive life include:

1. Non-volatile storage (NVS) full or not;

2. Amount of modified data in the stride;

3. Parity lock contention;

4. I/O is sequential or not;

5. Data sequentially written on disk or not;

6. Unmodified data already present in the stride or needs to be staged;

7. Bandwidth usage on the rank;

8. Number of I/O operations per second (IOPS) currently on the rank;

9. Number of holes in the stride;

10. Flash Wear Level;

11. Write Per Day classification of the drive;

12. Number of IOPS with full stride destage vs strip destage vsindividual track destage; and

13. Response time for stages/destages on the rank.

A customer may prefer to strike a balance between the drive life and theperformance of the destage operations. In certain embodiments, a storagecontroller determines whether to perform full stride destages or notperform full stride destages (e.g., strip destage or individual trackdestage) based on factors that affect performance and based on factorsthat affect the drive life.

Some of the factors influence drive endurance (i.e., life of the drives)whereas others affect performance. Certain customers may preferperformance over drive life whereas others may prefer drive life overperformance. Certain embodiments compute separate scores for performanceand drive life based on a plurality of factors and enable configurationsettings to decide which scores to favor for a particular customer.

As a result of the large number of factors that can influence thedecision to choose among full stride destages, strip destages, andindividual track destages, certain embodiments train a machine learningmodule (e.g., a neural network) to make the choice among full stridedestages, strip destages and individual track destages. As a result,improvements are made in the operations of a storage controller.

Exemplary Embodiments

FIG. 1 illustrates a block diagram of a computing environment 100comprising a storage controller 102 with a cache 104 and a non-volatilestorage (NVS) 106, coupled to one or more hosts 108 and one or morestorage devices 110, 112, in accordance with certain embodiments. Theone or more storage devices 110, 112 may form a RAID configured storage114.

The storage controller 102 allows the one or more hosts 108 to performinput/output (I/O) operations with logical storage maintained by thestorage controller 102. The physical storage corresponding to thelogical storage may be found in one or more of the storage devices 110,112 and/or cache 104 and/or non-volatile storage (NVS) 106 of thestorage controller 102.

The storage controller 102 and the hosts 108 may comprise any suitablecomputational device including those presently known in the art, suchas, a personal computer, a workstation, a server, a mainframe, a handheld computer, a palm top computer, a telephony device, a networkappliance, a blade computer, a processing device, a controller, etc. Incertain embodiments, the storage controller 102 may be comprised of aplurality of servers. The plurality of servers may provide redundancybecause if one server undergoes a failure from which recovery is notpossible, an alternate server may perform the functions of the serverthat failed. Each of the plurality of servers may be referred to as aprocessing complex and may include one or more processors and/orprocessor cores.

The storage controller 102 and the one or more hosts 108 may be elementsin any suitable network, such as, a storage area network, a wide areanetwork, the Internet, an intranet. In certain embodiments, storagecontroller 102 and the one or more hosts 108 may be elements in a cloudcomputing environment.

The cache 104 and the non-volatile storage 106 may be any suitablememory known in the art or developed in the future. The cache 104 andthe non-volatile storage 106 may be distributed among two servers in adual-server configuration of the storage controller 102. A destagemanagement application 116 that is implemented in software, hardware,firmware or any combination thereof in the storage controller 102 maycontrol destage operations from the storage controller 102 to secondarystorage comprising the storage devices 110, 112.

The plurality of storage devices 110, 112 may be comprised of anystorage devices known in the art. For example, the storage device 110may be a solid state drive (SSD) and the storage device 112 may be ahard disk drive (HDD).

A configuration data structure 118 that provides a weight forperformance of I/O operations (as shown via reference numeral 120) and aweight for drive life (as shown via reference numeral 122) may bemaintained by the storage controller 102, where the weights arepopulated by the a customer or administrator or user to indicate whetherto prioritize drive life over the performance of I/O operations orwhether to prioritize performance of I/O operations over drive life. Itshould writes lower the drive life of a drive (particularly in the caseof flash drives such as solid state drives).

In certain embodiments a machine learning module 124 may be implementedin software, hardware, firmware of any combination thereof inside oroutside the storage controller 102. In certain embodiments, the machinelearning module 124 is a neural network.

FIG. 2 illustrates a block diagram 200 that shows the types of destagesthat may be performed by the storage controller 102 that has stored datain a RAID configuration, in accordance with certain embodiments. Thetypes of destages include a full stride destage 202, a strip destage204, and individual track destage 206.

In full stride destage 202 all strides are destaged. In strip destage204 selected strips are destaged. In individual track destage 206,selected tracks are destaged. It should be noted that a strip iscomprised of a plurality of tracks.

FIG. 3 illustrates a block diagram 300 that shows a machine learningmodule 302 (corresponds to machine learning module 124 shown in FIG. 1)for determination of the proper destaging mechanism to balanceperformance and drive life, in accordance with certain embodiments. Theblock diagram 300 shows that the machine learning module 124 comprises amulti-output neural network 302.

The neural network 302 may comprise a collection of nodes with linksconnecting them, where the links are referred to as connections. Forexample, FIG. 3 shows a node 304 connected by a connection 308 to thenode 306. The collection of nodes may be organized into three mainparts: an input layer 310, one or more hidden layers, 312 and an outputlayer 314.

The connection between one node and another is represented by a numbercalled a weight, where the weight may be either positive (if one nodeexcites another) or negative (if one node suppresses or inhibitsanother). Training the neural network 302 entails calibrating theweights in the neural network 302 via mechanisms referred to as forwardpropagation 316 and back propagation 322. Bias nodes that are notconnected to any previous layer may also be maintained in the neuralnetwork 302. A bias is an extra input of 1 with a weight attached to itfor a node.

In forward propagation 316, a set of weights are applied to the inputdata 318, 320 to calculate outputs 324, 326, and 328. For the firstforward propagation, the set of weights are selected randomly. In backpropagation 322 a measurement is made the margin of error of the outputs324, 326, 328 and the weights are adjusted to decrease the error. Backpropagation 322 compares the outputs that the neural network 302produces with the output that the neural network 302 was meant toproduce, and uses the difference between them to modify the weights ofthe connections between the nodes of the neural network 302, startingfrom the output layer 314 through the hidden layers 312 to the inputlayer 310, i.e., going backward in the neural network 302. In time, backpropagation 322 causes the neural network 302 to learn, reducing thedifference between actual and intended outputs. Thus, the neural network302 is configured to repeat both forward and back propagation until theweights (and potentially the biases) of the neural network 302 arecalibrated to accurately predict an output.

In certain embodiments, the machine learning module 302 may beimplemented in software, firmware, hardware or any combination thereof.For example, in one embodiment the machine learning module 302 may beimplemented only in software, whereas in another embodiment the machinelearning module 302 may be implemented in a combination of software,firmware, and hardware. In one embodiment, each node of the machinelearning module 302 may be a lightweight hardware processor (e.g., a1-bit processor) and there may be hardwired connections among thelightweight hardware processors. Software and/or firmware may implementthe adjustment of weights of the links via adjustments in signalspropagated via the hardwired connections.

In certain embodiments, the plurality of inputs 318, 320 comprise aplurality of system parameters of the computing environment 100 thatcorrespond to factors that affect performance and drive life. Theoutputs 324, 326, 328 may provide indications of a measure ofsuitability of full stride destage, strip destage, and individual trackdestage respectively.

In certain embodiments, the machine learning module 302 is trained toimprove the determination of the proper destage mechanism for thestorage controller to balance performance with drive life. The trainingcontinuously improves the predictive ability of the machine learningmodule 302 over time.

FIG. 4 illustrates a block diagram 400 that shows inputs for the machinelearning module 302. The inputs for the machine learning module 302include the following:

1. NVS full or not (reference numeral 402);

2. Amount of modified data in the stride (reference numeral 404);

3. Parity lock contention (reference numeral 406);

4. I/O is sequential or not (reference numeral 408);

5. Data sequentially written on disk or not (reference numeral 410);

6. Unmodified data already present in the stride or needs to be staged(reference numeral 412);

7. Bandwidth usage on the rank (reference numeral 414);

8. Number of IOPS currently on the rank (reference numeral 416);

9. Number of holes in the stride (reference numeral 418).

10. Flash Wear Level (reference numeral 420);

11. Write Per Day classification of the drive (reference numeral 422);

12. Number of IOPS with full stride destage vs strip destage vsindividual track destage (reference numeral 424); and

13. Response time for stages/destages on the rank (reference numeral426).

Each of the performance factors may directly or indirectly affects theperformance of disk I/O during destage operations. In alternativeembodiments other factors or additional factors may be included in theinputs for the machine learning module 302.

FIG. 5 illustrates a block diagram 500 that shows outputs for themachine learning module 302.

The outputs of the machine learning module 302 include an output forfull stride destage (reference number 324), an output for strip destage(reference numeral 326) and an output for individual track destage(reference numeral 328). The outputs may range from 0 to 1, and theoutput that is highest indicates the mechanism to use for destage. Forexample, the machine learning module 302 may indicate that the outputfor full stride destage is 0.7, the output for strip destage is 0.5, andthe output for individual track destage is 0.1, and in such embodiments,the full stride destage mechanism is used.

FIG. 6 illustrates a block diagram that shows training of the machinelearning module 302.

Control starts at block 602 in which a predetermined number of I/Ooperations have occurred. The machine learning module 302 provides (atblock 604) via forward propagation, the output values 324, 326, 328. Themargin of error is computed and back propagation is used (at block 608)to train the machine learning module 302. The margin of error is thedeviation of the actual output from the expected output of the machinelearning module 302, and the machine learning module 1302 s attempts toreduce the error while adjusting the weights and biases.

When the machine learning module 302 is used in forward propagation,inputs and outputs are saved (for example save 100 inputs and outputs inthe last interval). Then expected outputs are computed. The learningmodule 302 may be trained by sing the difference between expectedoutputs and actual outputs.

FIG. 7 illustrates a block diagram 700 that shows factors forcalculating margin of error in the machine learning module 302, inaccordance with certain embodiments.

The computing of a margin of error to adjust weights in the machinelearning module includes comparing a maximum bandwidth to a currentbandwidth (block 702), comparing a maximum I/O operations per second(IOPS) to a current IOPS (block 704), comparing an optimal drive writemeasure to a measure of actual writes (block 706), and comparing ameasure of optimal parity lock contention to a parity lock contention(block 708). The optimal drive write may be in accordance with the“write per day classification” and the optimal parity lock contentionmay be 1% or some other percentage of a spin lock time.

FIGS. 8-11 provides details for operations shown in blocks 702, 704,706, 708 of FIG. 7. The operations are shown in pseudocode andself-explanatory. It should be noted that alternative embodiments mayperform the margin of error computations in a different way, and theones shown in FIGS. 8-11 are examples. In the pseudocode “Max” is anabbreviation for maximum.

FIG. 8 illustrates a block diagram 800 that shows adjustments made in amachine learning module based on bandwidth, in accordance with certainembodiments. The psuedo-code is as follows:

If current bandwidth>Max bandwidth then

Expected output for FullStrideScore=0

Expected output for StripScore=0

Expected output for IndividualTrackScore=1

Else

FullStrideScore=(MaxBandwidth−Current Bandwidth)/MaxBandwidth

StripScore=(MaxBandwidth−Current Bandwidth)/(MaxBandwidth×2)

IndividualTrackScore=1−((MaxBandwidth−Current Bandwidth)/MaxBandwidth)

FIG. 9 illustrates a block diagram 900 that shows adjustments made in amachine learning module based on I/O operations per second (IOPS), inaccordance with certain embodiments. The psuedo-code is as follows:

If current IOPS>Max IOPS then

Expected output for FullStrideScore=1

Expected output for StripScore=1

Expected Output for lndividualTrackScore=0

Else

FullStrideScore=1−(MaxIOPS−Current IOPS)/MaxIOPS

StripScore=(1−(MaxIOPS−Current IOPS)/MaxIOPS)/2

IndividualTrackScore=(MaxIOPS−Current IOPS)/MaxIOPS

FIG. 10 illustrates a block diagram 1000 that shows adjustments made ina machine learning module based on drive writes, in accordance withcertain embodiments. A comparison is made of number of drive writes inthe last interval and the optimal drives a per drive classification. Thepsuedo-code is as follows:

If drive writes are greater than optimal writes then

Expected output for FullStrideScore=0

Expected output for StripScore=0

Expected Output for lndividualTrackScore=1

Else

FullStrideScore=(OptimalWrites−Actual Writes)/OptimalWrites

StripScore=(OptimalWrites−Actual Writes)/(OptimalWrites×2)

IndividualTrackScore=1−(OptimalWrites−Actual Writes)/OptimalWrites

FIG. 11 illustrates a block diagram 1100 that shows adjustments made inmachine learning module based on parity lock contention, in accordancewith certain embodiments. The psuedo-code is as follows:

If Parity Lock Contention is above optimal parity lock contention

Expected output for FullStrideScore=1

Expected output for StripScore=0

Expected Output for lndividualTrackScore=0

Else

FullStrideScore=1−((OptimalParitylockContention−ActualParitylockContention)/OptimalParitylockContention)

StripScore=((OptimalParitylockContention−ActualParitylockContention)/OptimalParitylockContention)

IndividualTrackScore=((OptimalParitylockContention−ActualParitylockContention)/OptimalParitylockContention)

FIG. 12 illustrates a block diagram 1200 that adjusts weights fortraining the machine learning module based on weights provided by a userof performance and drive life, in accordance with certain embodiments.

The difference in expected output and actual output is changed based onperformance weight to train the learning module in the pseudo-code shownin FIG. 7-11 as follows:

Difference to train learning module=(Expected Score−ActualScore)*Performance Weight/Drive Life Weight.

Thus in certain embodiments, computing the margin of error to adjust theweights in the machine learning module 302 includes weightingadjustments to train the machine learning module based on a firstweightage provided for the performance of data transfer operations and asecond weightage provided for the preservation of drive life. Theseweightages may be provided by a user as shown in FIG. 1 referencenumerals 120, 122.

FIG. 13 illustrates a flowchart 1300 for selecting a type of destage toperform by the machine learning module, in accordance with certainembodiments. The operations shown in FIG. 13 may be performed in thestorage controller 102.

Control starts at block 1302, in which a storage controller 102 isconfigured to perform a full stride destage, a strip destage, and anindividual track destage. A machine learning module 124 (also shown viareference numeral 302) receives (at block 1304) a plurality of inputs318, 320 corresponding to a plurality of factors (shown in FIG. 4) thataffect performance of data transfer operations and preservation of drivelife in the storage controller 102.

From block 1304 control proceeds to block 1306 in which in response toreceiving the inputs, the machine learning module 124 generates a firstoutput 324, a second output 326, and a third output 328 that indicate apreference measure for the full stride destage, the strip destage, andthe individual track destage respectively. A determination (at block1308) of which type of destage to perform is based on the first output,the second output, and the third output.

Therefore, FIGS. 1-13 illustrate certain embodiments that use a learningmechanism to determine the type of destage to perform to adhere toweights for performance and weights for drive life provided in aconfiguration data structure.

While the embodiments described in FIGS. 1-13 show certain adjustmentsmade or values assigned to the variables FullStrideScore, theStripScore, and the IndividualTrackScore, in alternative embodimentsother adjustments or values may be used. There are other parameters inthe embodiments to which certain representative values have beenassigned, and these parameters may differ in alternative embodiments.

Cloud Computing Environment

Cloud computing is a model for enabling convenient, on-demand networkaccess to a shared pool of configurable computing resources (e.g.,networks, servers, storage, applications, and services) that can berapidly provisioned and released with minimal management effort orservice provider interaction.

Referring now to FIG. 14 an illustrative cloud computing environment 50is depicted. As shown, cloud computing environment 50 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone MA, desktop computer 54B, laptop computer MC,and/or automobile computer system MN may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 14 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 10 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 15, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 14) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 15 are intended to be illustrative only and embodiments ofthe invention are not limited thereto.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM zSeries* systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries* systems; IBMxSeries* systems; IBM BladeCenter* systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere*application server software; and database software, in one example IBMDB2* database software.

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and destage management via learning module 68 as shown inFIGS. 1-15.

Additional Embodiment Details

The described operations may be implemented as a method, apparatus orcomputer program product using standard programming and/or engineeringtechniques to produce software, firmware, hardware, or any combinationthereof. Accordingly, aspects of the embodiments may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the embodiments may take the form of a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentembodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present embodiments.

Aspects of the present embodiments are described herein with referenceto flowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instruction.

FIG. 16 illustrates a block diagram that shows certain elements that maybe included in the storage controller 102 or the host 108, or othercomputational devices in accordance with certain embodiments. The system1600 may include a circuitry 1602 that may in certain embodimentsinclude at least a processor 1604. The system 1600 may also include amemory 1606 (e.g., a volatile memory device), and storage 1608. Thestorage 1608 may include a non-volatile memory device (e.g., EEPROM,ROM, PROM, flash, firmware, programmable logic, etc.), magnetic diskdrive, optical disk drive, tape drive, etc. The storage 1608 maycomprise an internal storage device, an attached storage device and/or anetwork accessible storage device. The system 1600 may include a programlogic 1610 including code 1612 that may be loaded into the memory 1606and executed by the processor 1604 or circuitry 1602. In certainembodiments, the program logic 1610 including code 1612 may be stored inthe storage 1608. In certain other embodiments, the program logic 1610may be implemented in the circuitry 1602. One or more of the componentsin the system 1600 may communicate via a bus or via other coupling orconnection 1614. Therefore, while FIG. 16 shows the program logic 1610separately from the other elements, the program logic 1610 may beimplemented in the memory 1606 and/or the circuitry 1602.

Certain embodiments may be directed to a method for deploying computinginstruction by a person or automated processing integratingcomputer-readable code into a computing system, wherein the code incombination with the computing system is enabled to perform theoperations of the described embodiments.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s)” unless expressly specifiedotherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expresslyspecified otherwise.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or moreintermediaries.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the presentinvention.

Further, although process steps, method steps, algorithms or the likemay be described in a sequential order, such processes, methods andalgorithms may be configured to work in alternate orders. In otherwords, any sequence or order of steps that may be described does notnecessarily indicate a requirement that the steps be performed in thatorder. The steps of processes described herein may be performed in anyorder practical. Further, some steps may be performed simultaneously.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the present inventionneed not include the device itself.

At least certain operations that may have been illustrated in thefigures show certain events occurring in a certain order. In alternativeembodiments, certain operations may be performed in a different order,modified or removed. Moreover, steps may be added to the above describedlogic and still conform to the described embodiments. Further,operations described herein may occur sequentially or certain operationsmay be processed in parallel. Yet further, operations may be performedby a single processing unit or by distributed processing units.

The foregoing description of various embodiments of the invention hasbeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseform disclosed. Many modifications and variations are possible in lightof the above teaching. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto. The above specification, examples and data provide acomplete description of the manufacture and use of the composition ofthe invention. Since many embodiments of the invention can be madewithout departing from the spirit and scope of the invention, theinvention resides in the claims hereinafter appended.

* IBM, zSeries, pSeries, xSeries, BladeCenter, WebSphere, and DB2 aretrademarks of International Business Machines Corporation registered inmany jurisdictions worldwide.

What is claimed is:
 1. A method, comprising: configuring a storagecontroller to perform a full stride destage, a strip destage, and anindividual track destage; receiving, by a machine learning module, aplurality of inputs corresponding to a plurality of factors that affectperformance of data transfer operations and preservation of drive lifein the storage controller; in response to receiving the inputs,generating, by the machine learning module, a first output, a secondoutput, and a third output that indicate a preference measure for thefull stride destage, the strip destage, and the individual track destagerespectively; and determining which type of destage to perform based onthe first output, the second output, and the third output, andperforming the determined type of destage, the method further comprisingdetermining a margin of error to adjust weights in the machine learningmodule, wherein determining of the margin of error includes comparing amaximum bandwidth to a current bandwidth, and in response to determiningthat the current bandwidth is greater than the maximum bandwidth,setting a first expected output corresponding to the first output and asecond expected output corresponding to the second output to a firstvalue, and setting a third expected output corresponding to the thirdoutput to a second value that is greater than the first value.
 2. Themethod of claim 1, wherein the plurality of factors includesinput/output (I/O) operations and bandwidth on a rank, non-volatilestorage (NVS) capacity, parity lock contention, holes, unmodified datain a stride.
 3. The method of claim 2, wherein the plurality of factorsalso includes a wear level of a drive, writes per day classification ofthe drive, and an amount of modified data in the stride.
 4. The methodof claim 1, wherein computing of the margin of error to adjust weightsin the machine learning module includes comparing a maximum I/Ooperations per second (IOPS) to a current IOPS.
 5. The method of claim4, wherein computing the margin of error to adjust the weights in themachine learning module further includes weighting adjustments to trainthe machine learning module based on a first weightage provided for theperformance of data transfer operations and a second weightage providedfor the preservation of drive life.
 6. The method of claim 1, whereinthe machine learning module is a neural network with one or more hiddenlayers, and wherein forward and backward propagation mechanisms areutilized to adjust weights in the neural network.
 7. The method of claim1, wherein in response to determining that the current bandwidth is lessthan or equal to the maximum bandwidth, setting the first output to adifference of the maximum bandwidth and the current bandwidth divided bythe maximum bandwidth, setting the second output to a difference betweenthe maximum bandwidth and the current bandwidth divided by a multiple ofthe maximum bandwidth, and setting the third output to a differencebetween 1 and a difference of the maximum bandwidth and the currentbandwidth divided by the maximum bandwidth.
 8. A system, comprising: amemory; and a processor coupled to the memory, wherein the processorperforms operations, the operations performed by the processorcomprising: configuring the system to perform a full stride destage, astrip destage, and an individual track destage; receiving, by a machinelearning module, a plurality of inputs corresponding to a plurality offactors that affect performance of data transfer operations andpreservation of drive life in the system; in response to receiving theinputs, generating, by the machine learning module, a first output, asecond output, and a third output that indicate a preference measure forthe full stride destage, the strip destage, and the individual trackdestage respectively; and determining which type of destage to performbased on the first output, the second output, and the third output, andperforming the determined type of destage, the operations furthercomprising determining a margin of error to adjust weights in themachine learning module, wherein determining of the margin of errorincludes comparing a maximum bandwidth to a current bandwidth, and inresponse to determining that the current bandwidth is greater than themaximum bandwidth, setting a first expected output corresponding to thefirst output and a second expected output corresponding to the secondoutput to a first value, and setting a third expected outputcorresponding to the third output to a second value that is greater thanthe first value.
 9. The system of claim 8, wherein the plurality offactors includes input/output (I/O) operations and bandwidth on a rank,non-volatile storage (NVS) capacity, parity lock contention, holes,unmodified data in a stride.
 10. The system of claim 9, wherein theplurality of factors also includes a wear level of a drive, writes perday classification of the drive, and an amount of modified data in thestride.
 11. The system of claim 8, wherein computing of the margin oferror to adjust weights in the machine learning module includescomparing a maximum I/O operations per second (IOPS) to a current IOPS.12. The system of claim 11, wherein computing the margin of error toadjust the weights in the machine learning module further includesweighting adjustments to train the machine learning module based on afirst weightage provided for the performance of data transfer operationsand a second weightage provided for the preservation of drive life. 13.The system of claim 8, wherein the machine learning module is a neuralnetwork with one or more hidden layers, and wherein forward and backwardpropagation mechanisms are utilized to adjust weights in the neuralnetwork.
 14. The system of claim 8, wherein in response to determiningthat the current bandwidth is less than or equal to the maximumbandwidth, setting the first output to a difference of the maximumbandwidth and the current bandwidth divided by the maximum bandwidth,setting the second output to a difference between the maximum bandwidthand the current bandwidth divided by a multiple of the maximumbandwidth, and setting the third output to a difference between 1 and adifference of the maximum bandwidth and the current bandwidth divided bythe maximum bandwidth.
 15. A computer program product, the computerprogram product comprising a computer readable storage medium havingcomputer readable program code embodied therewith, the computer readableprogram code configured to perform operations, the operationscomprising: configuring a storage controller to perform a full stridedestage, a strip destage, and an individual track destage; receiving, bya machine learning module, a plurality of inputs corresponding to aplurality of factors that affect performance of data transfer operationsand preservation of drive life in the storage controller; in response toreceiving the inputs, generating, by the machine learning module, afirst output, a second output, and a third output that indicate apreference measure for the full stride destage, the strip destage, andthe individual track destage respectively; and determining which type ofdestage to perform based on the first output, the second output, and thethird output, and performing the determined type of destage, theoperations further comprising determining a margin of error to adjustweights in the machine learning module, wherein determining of themargin of error includes comparing a maximum bandwidth to a currentbandwidth, and in response to determining that the current bandwidth isgreater than the maximum bandwidth, setting a first expected outputcorresponding to the first output and a second expected outputcorresponding to the second output to a first value, and setting a thirdexpected output corresponding to the third output to a second value thatis greater than the first value.
 16. The computer program product ofclaim 15, wherein the plurality of factors includes input/output (I/O)operations and bandwidth on a rank, non-volatile storage (NVS) capacity,parity lock contention, holes, unmodified data in a stride.
 17. Thecomputer program product of claim 16, wherein the plurality of factorsalso includes a wear level of a drive, writes per day classification ofthe drive, and an amount of modified data in the stride.
 18. Thecomputer program product of claim 15, wherein computing of the margin oferror to adjust weights in the machine learning module includescomparing a maximum I/O operations per second (IOPS) to a current IOPS.19. The computer program product of claim 18, wherein computing themargin of error to adjust the weights in the machine learning modulefurther includes weighting adjustments to train the machine learningmodule based on a first weightage provided for the performance of datatransfer operations and a second weightage provided for the preservationof drive life.
 20. The computer program product of claim 15, wherein inresponse to determining that the current bandwidth is less than or equalto the maximum bandwidth, setting the first output to a difference ofthe maximum bandwidth and the current bandwidth divided by the maximumbandwidth, setting the second output to a difference between the maximumbandwidth and the current bandwidth divided by a multiple of the maximumbandwidth, and setting the third output to a difference between 1 and adifference of the maximum bandwidth and the current bandwidth divided bythe maximum bandwidth.